Abstract #0820

Prostate Lesion Segmentation on VERDICT-MRI Driven by Unsupervised Domain Adaptation

Eleni Chiou^1,2, Francesco Giganti^3,4, Shonit Punwani⁵, Iasonas Kokkinos², and Eleftheria Panagiotaki^1,2

¹Centre of Medical Image Computing, University College London, London, United Kingdom, ²Department of Computer Science, University College London, London, United Kingdom, ³Department of Radiology, UCLH NHS Foundation Trust, University College London, London, United Kingdom, ⁴Division of Surgery & Interventional Science, University College London, London, United Kingdom, ⁵Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom

In this work we utilize unsupervised domain adaptation for prostate lesion segmentation on VERDICT-MRI. Specifically, we use an image-to-image translation method to translate multiparametric-MRI data to the style of VERDICT-MRI. Given a successful translation we use the synthesized data to train a model for lesion segmentation on VERDICT-MRI. Our results show that this approach performs well on VERDICT-MRI despite the fact that it does not exploit any manual annotations.

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